This paper proposes a task-specific trajectory optimization framework for human-robot collaboration, enabling adaptive motion planning based on human interaction dynamics. Unlike conventional approaches that rely on predefined desired trajectories, the proposed framework optimizes the collaborative motion dynamically using the inverse differential Riccati equation, ensuring adaptability to task variations and human input. The generated trajectory serves as the reference for a neuro-adaptive PID controller, which leverages a neural network to adjust control gains in real time, addressing system uncertainties while maintaining low computational complexity. The combination of trajectory planning and the adaptive control law ensures stability and accurate joint-space tracking without requiring extensive parameter tuning. Numerical simulations validate the proposed approach.

翻译：本文提出了一种面向任务的人机协作轨迹优化框架，能够基于人机交互动力学实现自适应运动规划。与依赖预定义期望轨迹的传统方法不同，该框架利用逆微分Riccati方程动态优化协作运动，确保了对任务变化和人类输入的适应性。生成的轨迹作为神经自适应PID控制器的参考输入，该控制器通过神经网络实时调整控制增益，在保持较低计算复杂度的同时处理系统不确定性。轨迹规划与自适应控制律的结合保证了系统稳定性与精确的关节空间跟踪性能，且无需大量参数调优。数值仿真验证了所提方法的有效性。